0.23466511284265967
2023-06-05
Note
Testing callouts with simple appearance
Magnetic Moment of the Muon
\(a_{\mu} = \frac{(g_{\mu} - 2)}{2}\)
Can Lattice QCD resolve this?
new physics??
Idea: We can evolve the \((\dot{x}, \dot{v})\) system to get new states \(\\{x_{i}\\}\)❗
Write the Joint distribution \(p(x, v)\): \[p(x, v) = p(x) p(v) \propto e^{-S[x]} e^{-\frac{1}{2}v^{T} v} = e^{-H(x, v)}\]
Hamiltonian Dynamics: \[\begin{align} \dot{x} &= +\partial_{v} H\\ \dot{v} &= -\partial_{x} H \end{align}\]
Left column
Right column
For a demonstration of a line plot on a polar axis, see Figure 1
import numpy as np
import matplotlib.pyplot as plt
plt.rcParams.update({
'axes.facecolor': 'none',
'figure.facecolor': 'none',
'savefig.facecolor': 'none',
'savefig.format': 'svg',
'axes.edgecolor': 'none',
'axes.grid': True,
'axes.labelcolor': '#666',
'axes.titlecolor': '#666',
'grid.color': '#666',
'text.color': '#666',
'grid.linestyle': '--',
'grid.linewidth': 0.5,
'grid.alpha': 0.4,
'xtick.color': 'none',
'ytick.color': 'none',
'xtick.labelcolor': '#666',
'legend.edgecolor': 'none',
'ytick.labelcolor': '#666',
'savefig.transparent': True,
})
r = np.arange(0, 2, 0.01)
theta = 2 * np.pi * r
fix, ax = plt.subplots(
subplot_kw = {'projection': 'polar'}
)
assert isinstance(ax, plt.PolarAxes)
ax.plot(theta, r)
ax.set_rticks([0.5, 1., 1.5, 2.])
ax.grid(True)
plt.show()flowchart TB A --> C A --> D B --> C B --> D
flowchart LR markdown["`This **is** _Markdown_`"] newLines["`Line1 Line 2 Line 3`"] markdown --> newLines
List One
List Two
Colorbars indicate the quantitative extent of image data. Placing in a figure is non-trivial because room needs to be made for them. The simplest case is just attaching a colorbar to each axes:
import matplotlib.pyplot as plt
import numpy as np
fig, axs = plt.subplots(2, 2)
assert isinstance(fig, plt.Figure)
cmaps = ['RdBu_r', 'viridis']
for col in range(2):
for row in range(2):
ax = axs[row, col]
pcm = ax.pcolormesh(
np.random.random((20, 20)) * (col + 1),
cmap=cmaps[col]
)
fig.colorbar(pcm, ax=ax)
ax.grid(False)
plt.show()\[ y = w_0 * x_0 + w_1 * x_1 + w_2 * x_2 \]
GPU1GPU2flowchart LR
subgraph X0["GPU0"]
direction LR
a["w0"]
'end'
subgraph X1["GPU1"]
direction LR
b["w1"]
'end'
subgraph X2["GPU2"]
direction LR
c["w2"]
'end'
X1 & X0 <--> X2
X0 <--> X1
x["x0, x1, x2"] --> X0
Default
Note that there are six types of callouts, including: default, primary, success, info, and warning, and danger
Primary
Info
Success
Warning!
Danger!